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Trend vs scores

(figure out which regions to label)

index <- read.csv("data/scores_eez2018.csv") %>%
  select(region_id, region_name, Index)

vaxis <- index$Index[index$region_id==0]

trend <- read.csv(sprintf("data/trends_%s.csv", scenario)) %>%
  select(region_id, index_trend = Index) %>%
  left_join(index, by= "region_id") %>%
  filter(region_id != 0)


mod <- lm(index_trend ~ Index, data=trend)
summary(mod) # p value 0.015, adjusted R squared 0.022
## 
## Call:
## lm(formula = index_trend ~ Index, data = trend)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.54229 -0.38858  0.08531  0.46009  2.06282 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -1.158572   0.360088  -3.217  0.00149 **
## Index        0.013122   0.005343   2.456  0.01484 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7521 on 218 degrees of freedom
## Multiple R-squared:  0.02692,    Adjusted R-squared:  0.02246 
## F-statistic: 6.031 on 1 and 218 DF,  p-value: 0.01484
p <- ggplot2::ggplot(trend, aes(x=Index, y=index_trend)) +
    geom_point(aes(text=paste0("rgn = ", region_name)), shape=19, size=2, color="gray", alpha = 0.75) +
    geom_hline(yintercept = 0, color="red", size=.5, linetype=3) +
    geom_vline(xintercept = vaxis, color="red", size=.5, linetype=3) +
    stat_smooth(method=lm, se=FALSE, color="black", size=0.5) +
    theme_bw() + 
    labs(y="Average change in OHI score per year)", x="OHI score (2018)") 
## Warning: Ignoring unknown aesthetics: text
plotly_fig <- plotly::ggplotly(p)
plotly_fig
### adding labels: outliers, no change, etc
  
labels1 <- c("Eritrea", "Syria", "Equatorial Guinea", "Somalia", "Nicaragua", "Myanmar", "British Virgin Islands", "Georgia", "Pitcairn", "Liberia", "Sierra Leone")

labels2 <- c("Amsterdam Island and Saint Paul Island", "Kerguelen Islands")

## make sure region labels are spelled correctly  
setdiff(labels1, trend$region_name)
## character(0)
setdiff(labels2, trend$region_name)
## character(0)
## add labels column, give NA to those without labels
trend <- trend %>%
  mutate(label1 = ifelse(region_name %in% labels1, as.character(region_name), NA)) %>%
  mutate(label2 = ifelse(region_name %in% labels2, as.character(region_name), NA)) 
  

p <- ggplot2::ggplot(trend, aes(x=Index, y=index_trend)) +
    geom_point(shape=19, size=1.75, color="black", alpha = 0.5) +
    geom_text(aes(label=label1), hjust=0, nudge_x=0.4, size=2.2) + 
    geom_text(aes(label=label2), vjust=0, nudge_y=0.1, nudge_x=-5, size=2.2) +
   geom_point(data=filter(trend, region_name %in% label1), aes(x=Index, y=index_trend), shape=19, size=1.75, color="darkorange") +
   geom_point(data=filter(trend, region_name %in% label2), aes(x=Index, y=index_trend), shape=19, size=1.75, color="darkorange") +
    geom_hline(yintercept = 0, color="red", size=.2, linetype=3) +
    geom_vline(xintercept = vaxis, color="red", size=.2, linetype=3) +
      stat_smooth(method=lm, se=FALSE, color="black", size=0.3) +
    theme_bw() + 
    labs(y="Average change in Index score per year", x="OHI score, 2018") 

plot(p)  
## Warning: Removed 209 rows containing missing values (geom_text).
## Warning: Removed 218 rows containing missing values (geom_text).

ggsave("figures/trend vs score.png", width=7.5, height=5, units=c("in"), dpi=600)
## Warning: Removed 209 rows containing missing values (geom_text).

## Warning: Removed 218 rows containing missing values (geom_text).
ggsave("figures/trend vs score.tiff", width=6, height=4, units=c("in"), dpi=375)
## Warning: Removed 209 rows containing missing values (geom_text).

## Warning: Removed 218 rows containing missing values (geom_text).

pairwise comparison of goal scores

index_2018 <- read.csv("data/scores_eez2018.csv") %>%
  filter(region_id != 0) %>%
  select(-c(region_name, region_id)) %>%
  select(-c(SPP, HAB, ECO, LIV, FIS, MAR, ICO, LSP, Index))

panel.cor <- function(x, y, digits=2, prefix="", cex.cor, ...){
  usr <- par("usr"); on.exit(par(usr))
  par(usr = c(0, 1, 0, 1))
  r <- abs(cor(x, y, use="na.or.complete"))
  txt <- format(c(r, 0.123456789), digits=digits)[1]
  txt <- paste(prefix, txt, sep="")
  if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
  text(0.5, 0.5, txt, cex = (cex.cor * r + .15)*3)
}

pairs(index_2018,
      lower.panel=panel.smooth, upper.panel=panel.cor, 
      pch=16, cex=.5)

## export as "pairwise.png" into the folder "Results/figures" with 700 x 700 dimensions


#seeing ones were significant
# panel.cor <- function(x, y, digits=2, prefix="", cex.cor)
# {
#   usr <- par("usr"); on.exit(par(usr))
#   par(usr = c(0, 1, 0, 1))
#   r <- abs(cor(x, y))
#   txt <- format(c(r, 0.123456789), digits=digits)[1]
#   txt <- paste(prefix, txt, sep="")
#   if(missing(cex.cor)) cex <- 0.8/strwidth(txt)
# 
#   test <- cor.test(x,y)
#   # borrowed from printCoefmat
#   Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
#                    cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1),
#                    symbols = c("***", "**", "*", ".", " "))
# 
#   text(0.5, 0.5, txt, cex = cex * r)
#   text(.8, .8, Signif, cex=cex, col=2)
# }
# pairs(index_2018,
#       lower.panel=panel.smooth, upper.panel=panel.cor)

Scores vs. population, HDI, and CHI

Plotting OHI scores against Human Development Index (HDI) and Cumulative Human Impact (CHI)

index <- read.csv("data/scores_eez2018.csv") %>%
  select(rgn_id = region_id, region_name, Index) %>%
  filter(rgn_id != 0) 

## No HDI data in v2018 so grabbing it from v2017
hdi <- read.csv("https://raw.githubusercontent.com/OHI-Science/ohiprep_v2018/master/globalprep/supplementary_information/v2017/HDI_data.csv") %>%
  select(rgn_id, hdi=X2015)

# coastal population
pop <- read.csv("https://raw.githubusercontent.com/OHI-Science/ohiprep_v2018/master/globalprep/mar_prs_population/v2018/output/mar_pop_25mi.csv") %>%
  filter(year == 2018) %>%
  select(rgn_id, coastal_pop = popsum) %>%
  mutate(log_coastal_pop = log(coastal_pop + 1))

## No CHI data in v2018 so grabbing it from v2016
chi <- read.csv("https://raw.githubusercontent.com/OHI-Science/ohiprep_v2018/master/globalprep/supplementary_information/v2016/CHI_data.csv") %>%
  filter(sp_type == "eez") %>%
  select(rgn_id, chi=mean)


data <- index %>%
  left_join(pop, by="rgn_id") %>%
  left_join(chi, by="rgn_id") %>%
  left_join(hdi, by="rgn_id")

data_noNA <- na.omit(data)

mod1 <- lm(Index ~ chi, data=data_noNA)
summary(mod1) # p-val=0.001, R2=0.065
## 
## Call:
## lm(formula = Index ~ chi, data = data_noNA)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.1316  -4.9666   0.3523   5.5230  17.8710 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  76.1736     3.9874  19.104  < 2e-16 ***
## chi          -3.3008     0.9925  -3.326  0.00112 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.025 on 143 degrees of freedom
## Multiple R-squared:  0.07179,    Adjusted R-squared:  0.0653 
## F-statistic: 11.06 on 1 and 143 DF,  p-value: 0.001121
mod2 <- lm(Index ~ hdi, data=data_noNA)
summary(mod2) # p-val=8.1e-13, R2=0.23
## 
## Call:
## lm(formula = Index ~ hdi, data = data_noNA)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.2142  -4.7412  -0.2563   5.0074  14.6670 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   40.807      2.892  14.111  < 2e-16 ***
## hdi           31.141      3.958   7.867 8.12e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.959 on 143 degrees of freedom
## Multiple R-squared:  0.3021, Adjusted R-squared:  0.2972 
## F-statistic: 61.89 on 1 and 143 DF,  p-value: 8.117e-13
mod3 <- lm(Index ~ log_coastal_pop, data=data_noNA)
summary(mod3) # p-val=0.23, R2=0.003
## 
## Call:
## lm(formula = Index ~ log_coastal_pop, data = data_noNA)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -22.2075  -4.8709  -0.0856   5.3353  17.4559 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      56.1961     5.7201   9.824   <2e-16 ***
## log_coastal_pop   0.4587     0.3773   1.216    0.226    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.287 on 143 degrees of freedom
## Multiple R-squared:  0.01023,    Adjusted R-squared:  0.003308 
## F-statistic: 1.478 on 1 and 143 DF,  p-value: 0.2261
mod4 <- lm(Index ~ chi + hdi, data=data_noNA)
summary(mod4) # p-val=1.5e-15, R2=0.37
## 
## Call:
## lm(formula = Index ~ chi + hdi, data = data_noNA)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.1300  -3.9255  -0.1816   4.8247  16.2296 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  54.2779     4.1725  13.009  < 2e-16 ***
## chi          -3.4737     0.8133  -4.271 3.54e-05 ***
## hdi          31.5437     3.7405   8.433 3.45e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.574 on 142 degrees of freedom
## Multiple R-squared:  0.3815, Adjusted R-squared:  0.3728 
## F-statistic:  43.8 on 2 and 142 DF,  p-value: 1.527e-15
mod5 <- lm(Index ~ log_coastal_pop + hdi, data=data_noNA)
summary(mod5) # p-val=6.9e-12, R2=0.29
## 
## Call:
## lm(formula = Index ~ log_coastal_pop + hdi, data = data_noNA)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.1978  -4.7416   0.0194   5.1816  14.7021 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      38.2751     5.3432   7.163 3.92e-11 ***
## log_coastal_pop   0.1803     0.3196   0.564    0.574    
## hdi              30.8871     3.9932   7.735 1.74e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.976 on 142 degrees of freedom
## Multiple R-squared:  0.3036, Adjusted R-squared:  0.2938 
## F-statistic: 30.96 on 2 and 142 DF,  p-value: 6.945e-12
mod6 <- lm(Index ~ log_coastal_pop + chi, data=data_noNA)
summary(mod6) # p-val=0.0026, R2=0.068
## 
## Call:
## lm(formula = Index ~ log_coastal_pop + chi, data = data_noNA)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.5066  -5.0122  -0.3562   5.6404  19.2367 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      69.6533     6.8763  10.129  < 2e-16 ***
## log_coastal_pop   0.4246     0.3651   1.163  0.24672    
## chi              -3.2681     0.9917  -3.295  0.00124 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.016 on 142 degrees of freedom
## Multiple R-squared:  0.08055,    Adjusted R-squared:  0.0676 
## F-statistic:  6.22 on 2 and 142 DF,  p-value: 0.002574
mod7 <- lm(Index ~ log_coastal_pop + chi + hdi, data=data_noNA)
summary(mod7) # p-val=1.04e-14, R2=0.37
## 
## Call:
## lm(formula = Index ~ log_coastal_pop + chi + hdi, data = data_noNA)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.1209  -4.0737  -0.4478   4.9092  16.2516 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      52.2643     6.0307   8.666 9.47e-15 ***
## log_coastal_pop   0.1401     0.3022   0.464    0.644    
## chi              -3.4619     0.8159  -4.243 3.98e-05 ***
## hdi              31.3452     3.7752   8.303 7.50e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.592 on 141 degrees of freedom
## Multiple R-squared:  0.3825, Adjusted R-squared:  0.3693 
## F-statistic: 29.11 on 3 and 141 DF,  p-value: 1.039e-14
# lowest AIC is model 4
AIC(mod1, mod2, mod3, mod4, mod5, mod6, mod7)
##      df       AIC
## mod1  3 1019.4358
## mod2  3  978.0908
## mod3  3 1028.7468
## mod4  4  962.5650
## mod5  4  979.7662
## mod6  4 1020.0609
## mod7  5  964.3441
mod8 <- lm(chi ~ log_coastal_pop, data=data)
summary(mod8) # p-val=5e-08, R2=0.12
## 
## Call:
## lm(formula = chi ~ log_coastal_pop, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.37233 -0.34773  0.05438  0.45822  2.73513 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      3.16549    0.13164  24.046  < 2e-16 ***
## log_coastal_pop  0.05495    0.00973   5.648 5.01e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6951 on 219 degrees of freedom
## Multiple R-squared:  0.1271, Adjusted R-squared:  0.1232 
## F-statistic:  31.9 on 1 and 219 DF,  p-value: 5.006e-08
mod9 <- lm(hdi ~ log_coastal_pop, data=data)
summary(mod9) # p-val=0.18, R2=0.006
## 
## Call:
## lm(formula = hdi ~ log_coastal_pop, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.30843 -0.10936  0.02832  0.11764  0.22860 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.580209   0.100829   5.754 5.09e-08 ***
## log_coastal_pop 0.009013   0.006651   1.355    0.178    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1461 on 143 degrees of freedom
##   (76 observations deleted due to missingness)
## Multiple R-squared:  0.01268,    Adjusted R-squared:  0.005775 
## F-statistic: 1.836 on 1 and 143 DF,  p-value: 0.1775
## Plot HDI against OHI Score
p <- ggplot2::ggplot(data, aes(x=hdi,y=Index)) +
    geom_point(shape=19, size=2, color="gray", alpha = 0.5) +
    theme_bw()  + 
    stat_smooth(method=lm, se=FALSE, color="black", size = 0.5)+
    labs(x="Human Development Index", y="OHI index score") 

plot(p)   
## Warning: Removed 76 rows containing non-finite values (stat_smooth).
## Warning: Removed 76 rows containing missing values (geom_point).

ggsave("figures/OHIvsHDI.png", height=4, width=4.5)
## Warning: Removed 76 rows containing non-finite values (stat_smooth).

## Warning: Removed 76 rows containing missing values (geom_point).
## Plot CHI against OHI Score
p <- ggplot2::ggplot(data, aes(y=Index, x=chi)) +
    geom_point(shape=19, size=2, color="gray", alpha = 0.5) +
    theme_bw()  + 
    stat_smooth(method=lm, se=FALSE, color="black", size = 0.5)+
    labs(x="Cumulative Human Impact", y="OHI index score") 

plot(p)   

ggsave("figures/OHIvsCHI.png", height=4, width=4.5)

## Plot Coastal Population against OHI Score
p <- ggplot2::ggplot(data, aes(y=Index, x=log_coastal_pop)) +
    geom_point(shape=19, size=2, color="gray", alpha = 0.5) +
    theme_bw()  + 
    stat_smooth(method=lm, se=FALSE, color="black", size = 0.5)+
    labs(x="Coastal population (ln)", y="OHI index score") 

plot(p)   

ggsave("figures/OHIvsCoastalPop.png", height=4, width=4.5)

## Plot Coastal Population against CHI
p <- ggplot2::ggplot(data, aes(x=chi, y=log_coastal_pop)) +
    geom_point(shape=19, size=2, color="gray", alpha = 0.5) +
    theme_bw()  + 
    stat_smooth(method=lm, se=FALSE, color="black", size = 0.5)+
    labs(x="Coastal population (ln)", y="Cumulative Human Impact") 

plot(p)   

ggsave("figures/CHIvsCoastalPop.png", height=4, width=4.5)

Trend analysis

### prepare data:
data <- read.csv(sprintf('../OHI_final_formatted_scores_%s.csv', saveDate)) 

data <- data %>%
  filter(dimension == "score") %>%   # focus only on score data
  filter(region_id != 0) %>%         # this weighted mean includes high seas and Antarctica
  filter(region_id <= 250) %>%       # get rid of high seas regions
  filter(region_id != 213) %>%       # get rid of antarctica
  filter(!is.na(value))
data_lm <- data %>%
  group_by(goal, region_id) %>%
  do(mdl = lm(value ~ scenario, data = .))

#data.frame(glance(data_lm, mdl))
results_lm <- tidy(data_lm, mdl) %>%
  ungroup() %>%
  filter(term == "scenario") %>%
  arrange(goal, estimate) %>%
  left_join(goal_names) %>%
  left_join(rgn_names) %>%
  select(goal=goal, goal_long=long_goal, region_name = country, region_id, average_change_per_year = estimate, p.value) %>%
  data.frame()
## Warning in summary.lm(x): essentially perfect fit: summary may be
## unreliable

## Warning in summary.lm(x): essentially perfect fit: summary may be
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## Warning in summary.lm(x): essentially perfect fit: summary may be
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## Joining, by = "goal"
## Joining, by = "region_id"
# 
# weights <- read.csv("../../eez2016/layers/rgn_area.csv") %>%
#   select(region_id=rgn_id, area_km2)
# 
# results_lm <- results_lm %>%
#   left_join(weights, by="region_id")
# 
# summary_lm <- results_lm %>%
#   group_by(goal, goal_long) %>%
#   summarize(mean=mean(average_change_per_year),
#             mean_wt=weighted.mean(average_change_per_year, w=area_km2))


# color coded histogram of trends

trends_hist <- results_lm %>%
  filter(goal=="Index") %>%
  arrange(average_change_per_year)

values <-   brewer.pal(11, "RdYlBu")
values <- colorRampPalette(values, space = 'Lab')(13)[2:13]
col.brks  <- c(-100, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 100)

trends_hist$colors <- cut(trends_hist$average_change_per_year, breaks=col.brks, include.lowest=TRUE, labels=values)
cols <- as.character(unique(trends_hist$colors))


g <- ggplot(trends_hist, aes(x=average_change_per_year)) +
geom_histogram(aes(fill=colors), bins=25, center=1.01, col="gray") +
scale_fill_manual("", breaks=colors, values=cols, guide=FALSE) +
labs(y="Number of regions", x="Average yearly change") +
#geom_vline(xintercept=0, color="red") +
theme_bw()

print(g)

ggsave("figures/trend_histogram_color.tiff", width=4, height=3, units=c("in"), dpi=300)
data_scores <- data %>%
  filter(goal=="Index") %>%
  filter(scenario == 2018) 

reds <-  colorRampPalette(c("#A50026", "#D73027", "#F46D43", "#FDAE61", "#FEE090"), space="Lab")(66)
blues <-  colorRampPalette(c("#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695"), space="Lab")(35)
colors <-   c(reds, blues)

col.brks <- seq(0,100, by=1)
col.assign <- cut(col.brks, breaks=col.brks, include.lowest = TRUE)
colors_df <- data.frame(colors_plot=colors, col.assign)
colors_df$col.assign <- as.character(colors_df$col.assign) 

data_scores$col.assign <- cut(data_scores$value, breaks=col.brks, include.lowest=TRUE) 
data_scores$col.assign <- as.character(data_scores$col.assign)

data_scores <- left_join(data_scores, colors_df, by="col.assign") %>%
  mutate(colors_plot = as.character(colors_plot)) %>%
  arrange(value)

cols <- as.character(unique(data_scores$colors_plot))

g <- ggplot(data_scores, aes(x=value)) +
#geom_histogram(aes(fill=colors), bins=25, center=1.01, col="gray") +
geom_histogram(aes(fill=col.assign), bins=31) +  
scale_fill_manual("", values=cols, guide=FALSE) +
xlim(c(0, 100)) +
labs(y="Number of regions", x="Index score") +
theme_bw()

print(g)

ggsave("figures/score_histogram_color.tiff", width=4, height=3, units=c("in"), dpi=300)
ggsave("figures/score_histogram_color.png", width=4, height=3, units=c("in"), dpi=300)